API Architecture

Swarms API Architecture

The Swarms API provides a comprehensive multi-tier architecture for building intelligent AI systems. The platform is designed around three distinct agent paradigms, each optimized for different types of tasks and complexity levels.

Architecture Tiers

Tier	Name	Agent Count	Complexity	Primary Use	Example Use Cases	API Endpoint
Tier 1	Individual Agents	1	Low-Medium	Focused tasks	Content generation, data analysis, Q&A	`/v1/agent/completions`
Tier 2	Reasoning Agents	1-2 (internal)	Medium-High	Complex reasoning	Mathematical proofs, logical validation, research	`/v1/reasoning-agent/completions`
Tier 3	Multi-Agent Swarms	3-10,000+	High	Enterprise workflows	Process automation, large-scale systems, R&D	`/v1/swarm/completions`

Tier 1: Individual Agents

Single-purpose AI agents for focused tasks Individual agents are the foundation of the Swarms ecosystem. These are custom-built, single-purpose AI agents designed to handle specific tasks with high precision and efficiency. Key Characteristics

Single Agent: One AI model per agent
Focused Purpose: Specialized for specific tasks
Customizable: Full control over system prompts, tools, and behavior
Efficient: Optimized for direct task execution
Scalable: Can be combined into larger systems

Use Cases

Content generation (articles, code, reports)
Data analysis and processing
Customer service responses
Creative tasks (writing, design)
Simple Q&A and information retrieval
Tool execution and automation

Example Implementation

import requests

payload = {
    "agent_config": {
        "agent_name": "content-writer",
        "description": "Professional content writer for technical articles",
        "system_prompt": "You are an expert technical writer...",
        "model_name": "gpt-4o",
        "max_tokens": 4000,
        "temperature": 0.7
    },
    "task": "Write a comprehensive guide on API security best practices"
}

response = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": "your-api-key"},
    json=payload
)

Tier 2: Reasoning Agents

Advanced reasoning systems for complex problem-solving Reasoning agents leverage sophisticated reasoning techniques to solve complex problems that require deep analysis, multiple perspectives, and systematic thinking. These agents may internally use 1-2 specialized sub-agents to achieve their reasoning goals. Key Characteristics

Reasoning-Focused: Built for complex logical and analytical tasks
Multi-Perspective: Can approach problems from different angles
Iterative: Capable of refinement and improvement cycles
Specialized Types: 7 different reasoning agent types available
Internal Coordination: May use sub-agents for specialized reasoning

Available Reasoning Agent Types

Agent Type	Description	Best For
reasoning-duo	Dual-agent system with perspective synthesis	Mathematical problems, logical proofs
self-consistency	Multiple reasoning paths with validation	Complex logical problems, consistency checking
ire	Iterative refinement approach	Complex analysis, research problems
reasoning-agent	General-purpose systematic reasoning	Step-by-step problem solving
consistency-agent	Logical consistency and contradiction detection	Argument validation
ReflexionAgent	Self-reflection and bias detection	Meta-cognitive tasks
GKPAgent	Cross-domain knowledge synthesis	Interdisciplinary problems

Use Cases

Mathematical proofs and complex calculations
Logical consistency validation
Research and analysis tasks
Cross-domain problem solving
Bias detection and ethical analysis
Iterative improvement scenarios

Example Implementation

payload = {
    "agent_name": "math-reasoner",
    "description": "Mathematical problem solver using dual perspectives",
    "model_name": "claude-3-5-sonnet-20240620",
    "system_prompt": "You are an expert mathematical reasoning agent...",
    "max_loops": 1,
    "swarm_type": "reasoning-duo",
    "task": "Prove that the sum of any three consecutive integers is divisible by 3"
}

response = requests.post(
    "https://api.swarms.world/v1/reasoning-agent/completions",
    headers={"x-api-key": "your-api-key"},
    json=payload
)

📖 Complete Reasoning Agents Documentation

Tier 3: Multi-Agent Swarms

Large-scale agent systems for complex workflows Multi-agent swarms represent the most sophisticated tier, capable of orchestrating anywhere from 3 to 10,000+ agents working together in coordinated workflows. These systems are designed for enterprise-scale applications and complex business processes. Key Characteristics

Massive Scale: 3 to 10,000+ agents per swarm
Coordinated Workflows: Agents work together in structured processes
Multiple Swarm Types: 12+ different swarm architectures available
Enterprise-Grade: Built for complex business applications
Dynamic Routing: Intelligent task distribution and agent selection

Available Swarm Types

Swarm Type	Description	Agent Count	Best For
SequentialWorkflow	Linear task progression	3-50	Process automation, step-by-step workflows
ConcurrentWorkflow	Parallel task execution	5-100	Parallel processing, independent tasks
GroupChat	Interactive agent discussions	3-20	Collaborative problem solving, brainstorming
MixtureOfAgents	Specialized agent selection	5-200	Complex tasks requiring multiple expertise areas
MajorityVoting	Consensus-based decision making	5-50	Decision making, validation tasks
CouncilAsAJudge	Expert panel with final judge	5-30	Expert evaluation, quality assessment
InteractiveGroupChat	Real-time agent interactions	3-15	Dynamic problem solving, real-time collaboration
AgentRearrange	Dynamic agent reordering	3-100	Adaptive workflows, optimization
MultiAgentRouter	Intelligent task routing	10-500	Large-scale task distribution
HiearchicalSwarm	Nested agent hierarchies	10-1000	Complex organizational structures
AutoSwarmBuilder	Automatic swarm construction	5-200	Dynamic swarm creation, optimization
MALT	Multi-agent learning and training	10-10000+	Large-scale learning systems

Use Cases

Enterprise process automation
Large-scale data processing
Complex decision-making systems
Research and development workflows
Customer service automation
Content creation pipelines
Quality assurance systems
Dynamic resource allocation

Example Implementation

payload = {
    "name": "Enterprise Content Pipeline",
    "description": "Multi-stage content creation and review system",
    "agents": [
        {
            "agent_name": "researcher",
            "description": "Research and gather information",
            "model_name": "gpt-4o",
            "role": "researcher"
        },
        {
            "agent_name": "writer",
            "description": "Create initial content",
            "model_name": "claude-3-5-sonnet-20240620",
            "role": "writer"
        },
        {
            "agent_name": "editor",
            "description": "Review and improve content",
            "model_name": "gpt-4o",
            "role": "editor"
        },
        {
            "agent_name": "fact-checker",
            "description": "Verify accuracy and sources",
            "model_name": "claude-3-5-sonnet-20240620",
            "role": "validator"
        }
    ],
    "max_loops": 2,
    "swarm_type": "SequentialWorkflow",
    "task": "Create a comprehensive industry report on AI trends in 2024"
}

response = requests.post(
    "https://api.swarms.world/v1/swarm/completions",
    headers={"x-api-key": "your-api-key"},
    json=payload
)

Architecture Comparison

Aspect	Individual Agents	Reasoning Agents	Multi-Agent Swarms
Agent Count	1	1-2 (internal)	3-10,000+
Complexity	Low-Medium	Medium-High	High-Extreme
Use Case	Focused tasks	Complex reasoning	Enterprise workflows
Setup Time	Minutes	Minutes-Hours	Hours-Days
Resource Usage	Low	Medium	High
Scalability	Individual	Limited	Massive
Cost	Low	Medium	High
Maintenance	Simple	Moderate	Complex

Choosing the Right Architecture

When to Use Individual Agents

✅ Single, well-defined tasks
✅ Quick prototyping and testing
✅ Resource-constrained environments
✅ Simple automation needs
✅ Cost-sensitive applications

When to Use Reasoning Agents

✅ Complex problem-solving tasks
✅ Tasks requiring multiple perspectives
✅ Logical consistency validation
✅ Research and analysis work
✅ Tasks requiring iterative improvement

When to Use Multi-Agent Swarms

✅ Complex business processes
✅ Large-scale automation
✅ Multi-step workflows
✅ Enterprise applications
✅ Tasks requiring multiple expertise areas
✅ Dynamic, adaptive systems

Integration Patterns

Hybrid Approaches

You can combine different tiers for optimal results:

Individual + Reasoning: Use individual agents for data collection, reasoning agents for analysis
Reasoning + Swarms: Use reasoning agents within swarms for complex decision-making
All Three Tiers: Individual agents for data processing, reasoning agents for analysis, swarms for orchestration

Migration Paths

Start Simple: Begin with individual agents, upgrade to reasoning agents for complex tasks
Scale Up: Move from reasoning agents to swarms for enterprise needs
Optimize: Use reasoning agents within swarms for enhanced decision-making

Performance Considerations

Individual Agents

Latency: 1-5 seconds
Throughput: High (1000+ requests/minute)
Cost: $0.01-0.10 per request
Memory: Minimal

Reasoning Agents

Latency: 5-30 seconds
Throughput: Medium (100-500 requests/minute)
Cost: $0.05-0.50 per request
Memory: Moderate

Multi-Agent Swarms

Latency: 30 seconds - 10 minutes
Throughput: Variable (10-100 requests/minute)
Cost: $0.10-5.00 per request
Memory: High

Best Practices

1. Start with the Right Tier

Begin with individual agents for simple tasks
Upgrade to reasoning agents when complexity increases
Use swarms only when necessary for scale

2. Optimize for Your Use Case

Match agent capabilities to task requirements
Consider cost vs. performance trade-offs
Plan for scalability from the start

3. Monitor and Iterate

Track performance metrics across all tiers
Optimize based on usage patterns
Consider hybrid approaches for complex needs

Getting Started

Quick Start Guide

Get API Key: https://swarms.world/platform/api-keys
Choose Your Tier: Start with individual agents for simple tasks
Build and Test: Create your first agent and test functionality
Scale Up: Move to reasoning agents or swarms as needed

Documentation Resources

Individual Agents: Agents Overview
Reasoning Agents: Reasoning Agents Documentation
Multi-Agent Swarms: Swarm Types and Configurations
API Reference: Complete API Documentation

Support and Community

Technical Support: Book a Call
Community: Join our Discord
Updates: Follow us on Twitter

For enterprise deployments and custom solutions, contact our team for dedicated support and consultation.

Getting Started

Resources

​API Architecture

​Swarms API Architecture

​Architecture Tiers

​Tier 1: Individual Agents

​Tier 2: Reasoning Agents

​Tier 3: Multi-Agent Swarms

​Architecture Comparison

​Choosing the Right Architecture

​When to Use Individual Agents

​When to Use Reasoning Agents

​When to Use Multi-Agent Swarms

​Integration Patterns

​Hybrid Approaches

​Migration Paths

​Performance Considerations

​Individual Agents

​Reasoning Agents

​Multi-Agent Swarms

​Best Practices

​1. Start with the Right Tier

​2. Optimize for Your Use Case

​3. Monitor and Iterate

​Getting Started

​Quick Start Guide

​Documentation Resources

​Support and Community

API Architecture

Swarms API Architecture

Architecture Tiers

Tier 1: Individual Agents

Tier 2: Reasoning Agents

Tier 3: Multi-Agent Swarms

Architecture Comparison

Choosing the Right Architecture

When to Use Individual Agents

When to Use Reasoning Agents

When to Use Multi-Agent Swarms

Integration Patterns

Hybrid Approaches

Migration Paths

Performance Considerations

Individual Agents

Reasoning Agents

Multi-Agent Swarms

Best Practices

1. Start with the Right Tier

2. Optimize for Your Use Case

3. Monitor and Iterate

Getting Started

Quick Start Guide

Documentation Resources

Support and Community